AI agents that run in production, with a human on the calls that matter
For teams who wired up the Claude or OpenAI API and hit the wall: hallucinations, cost spikes, silent failures. We build AI agents with the layer that makes them reliable, and we route the hard cases to a person instead of shipping a wrong answer.
You shipped a prototype. Then real traffic broke it.
You built something on the Claude or OpenAI API in a weekend. It demoed well. Then real usage hit and it fell apart: made-up answers, no retries, costs you could not predict, and no way to see why a run failed. The model was never the hard part. The agentic layer around it is: structured outputs, retry and fallback logic, evals, and a clear rule for when to escalate to a human. Most teams never build that layer, and generic frameworks will not give it to you for your specific process.
How we actually build it
We start by mapping one workflow end to end and defining what correct means, because you cannot improve what you cannot measure. Then we build the reliability layer: structured outputs so the agent returns typed data instead of loose prose, retry logic and fallback chains so one failed call does not kill the run, and RAG grounding so answers cite your data instead of the model's guesses. For multi-step work we use multi-agent setups where each agent owns one job, wired through n8n or direct code depending on latency needs. We connect the tools you already run (Apollo, Attio, PolyAI, your own APIs), add evals that score every change against a fixed test set, and set explicit escalation thresholds so low-confidence cases route to a person, not to production. You get a staging version to break before anything touches real users.
What you end up with
- —A working agent deployed into your stack, not a notebook or a slide deck.
- —Structured, typed outputs with retry logic and fallback chains, so a single failed API call does not take down the run.
- —An eval suite that scores accuracy against a fixed test set, so you can see the effect of every prompt or model change before it ships.
- —Defined escalation thresholds and a human review queue for low-confidence cases.
- —Integrations into the tools you run today (Apollo, Attio, n8n, PolyAI, or your own APIs) with logging so you can trace any run.
- —Documentation and a handoff so your team can change prompts and thresholds without us.
Proof of Work
AI Medical App & Personalized Patient CareAI Medical Triage
A digital health startup came to us for first-level medical guidance that would not add to clinical workload or cut corners on safety. We built an AI triage agent on a secure LLM architecture with symptom-based questioning, medically grounded reasoning, strict data isolation, and defined escalation thresholds: high-risk cases route to in-person care, low-risk cases get self-care guidance, and a clinician stays in the loop. The result was fewer repetitive physician inquiries, faster patient response times, and more consistent intake that scaled without added clinical headcount.
Read the case studyCommon Questions
Our process is too custom for an off-the-shelf agent. Does this still work?+
That is exactly why we build instead of resell a template. We model your specific workflow, your data, and your rules for what counts as correct. Custom is the point. Generic agent frameworks fail on custom processes because they have no opinion about your edge cases, so we encode yours directly.
What happens when the agent gets something wrong?+
We assume it will, and we design for it. Every agent has a confidence threshold: below it, the case routes to a human instead of going out wrong. In the medical triage build, high-risk cases always escalate to in-person care. We would rather the agent hand off the hard cases than guess, and you get logs to trace exactly why any run went the way it did.
We've had software projects that never shipped. Why is this different?+
We scope to one workflow and get it to staging in weeks, not a year-long platform build. You get something running that you can break and test before it touches real users. If the evals do not clear the bar you set, we do not ship it. Small, measurable, in production, then expand.
How do you start, and how long does it take?+
We start with the one workflow where the volume and the pain are highest. First we define what correct means and build an eval set, then we build the agent against it. Most first agents reach a working staged version in a few weeks, depending on how many systems it has to touch and how clean your data is.
Will this replace my team?+
No, and we are honest that it should not. These agents take repetitive judgment work off people so they can focus on the cases that need a human. The triage system was built to support clinicians, not stand in for them. The agent handles the routine volume and escalates the rest, and someone on your side still owns the exceptions.
Do you actually run agents yourselves, or just build them?+
We run them internally for our own sales, ops, and support before we recommend anything. We build on tools we use daily: Claude for reasoning, n8n for orchestration, Apollo and Attio for go-to-market data, PolyAI for voice, and RAG for grounding. You are not a test case for a pattern we have never run ourselves.
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